Wu Chuixiong, Li Ruye, Yu Kuang
Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Front Mol Biosci. 2022 May 19;9:851311. doi: 10.3389/fmolb.2022.851311. eCollection 2022.
Molecular mechanics (MM) is a powerful tool to study the properties of molecular systems in the fields of biology and materials science. With the development of ab initio force field and the application of ab initio potential energy surface, the nuclear quantum effect (NQE) is becoming increasingly important for the robustness of the simulation. However, the state-of-the-art path-integral molecular dynamics simulation, which incorporates NQE in MM, is still too expensive to conduct for most biological and material systems. In this work, we analyze the locality of NQE, using both analytical and numerical approaches, and conclude that NQE is an extremely localized phenomenon in nonreactive molecular systems. Therefore, we can use localized machine learning (ML) models to predict quantum force corrections both accurately and efficiently. Using liquid water as example, we show that the ML facilitated centroid MD can reproduce the NQEs in both the thermodynamical and the dynamical properties, with a minimal increase in computational time compared to classical molecular dynamics. This simple approach thus largely decreases the computational cost of quantum simulations, making it really accessible to the studies of large-scale molecular systems.
分子力学（MM）是研究生物学和材料科学领域分子系统性质的有力工具。随着从头算力场的发展以及从头算势能面的应用，核量子效应（NQE）对于模拟的稳健性变得越来越重要。然而，将NQE纳入MM的最先进的路径积分分子动力学模拟，对于大多数生物和材料系统来说，进行起来仍然过于昂贵。在这项工作中，我们使用解析和数值方法分析了NQE的局域性，并得出结论：在非反应性分子系统中，NQE是一种极其局域化的现象。因此，我们可以使用局域机器学习（ML）模型准确且高效地预测量子力修正。以液态水为例，我们表明，与经典分子动力学相比，ML辅助的质心分子动力学能够在热力学和动力学性质方面重现NQE，且计算时间增加极少。这种简单方法因此大幅降低了量子模拟的计算成本，使得大规模分子系统的研究真正可行。
